Morocco

Verifying the truthfulness of claims usually requires joint multi-modal reasoning over both textual and visual evidence, such as analyzing both textual caption and chart image for claim verification. In addition, to make the reasoning process transparent, a textual explanation is necessary to justify the verification result. However, most claim verification works mainly focus on the reasoning over textual evidence only or ignore the explainability, resulting in inaccurate and unconvincing verification. To address this problem, we propose a novel model that jointly achieves evidence retrieval, multi-modal claim verification, and explanation generation. For evidence retrieval, we construct a two-layer multi-modal graph for claims and evidence, where we design image-to-text and text-to-image reasoning for multi-modal retrieval. For claim verification, we propose token- and evidence-level fusion to integrate claim and evidence embeddings for multi-modal verification. For explanation generation, we introduce multi-modal Fusion-in-Decoder for explainability. Finally, since almost all the datasets are in general domain, we create a scientific dataset, AIChartClaim, in AI domain to complement claim verification community. Experiments show the strength of our model. Code and data are submitted. We will release them upon publication.

EACL 2026

MEVER: Multi-Modal and Explainable Claim Verification with Graph-based Evidence Retrieval

multi-modality

misinformation detection

claim verification

fact-checking

## Welcome to EMNLP 2024! 
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EMNLP 2024

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I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

The abundance of multimodal news in digital form has intensified demand for systems that condense articles and images into concise, faithful digests. Yet most approaches simply conduct unimodal text summarization and attach the most-similar images with the text summary, which leads to redundancy both in processing visual content as well as in selection of images to complement the summary. We propose MULSUM, a two-step framework: (i) a Cross-Vis Aligner that projects image-level embeddings into a shared space and conditions a pre-trained LLM decoder to generate a visually informed text summary, and (ii) a Diversity-Aware Image Selector that, after the summary is produced, maximizes images-relevance to the summary while enforcing pairwise image diversity, yielding a compact, complementary visual set. Experimental results on the benchmarks MSMO (Multimodal Summarization with Multimodal Output) corpus show that MULSUM consistently outperforms strong baselines on automatic metrics such as ROUGE, while qualitative inspection shows that selected images act as explanatory evidence rather than ornamental add-ons. Human evaluation results shows that our diverse set of selected images was 13% more helpful than mere similarity-based image selection.

MULSUM: A Multimodal Summarization System with Vis-Aligner and Diversity-Aware Image Selection

Large language models achieve strong performance through training on vast datasets. Can analogical paradigm organization enable lightweight models to match this performance with minimal data? We develop a computational approach implementing three cognitive-inspired principles: analogical structure, contrastive learning, and minimal contextual cues. We test this approach with structured completion tasks where models identify correct sentence completions from analogical patterns with contrastive alternatives. Training lightweight models (BERT+CNN, 0.5M parameters) on only one hundred structured examples of English causative/inchoative alternations achieves F1=0.95, outperforming zero-shot GPT-o3 (F1=0.87). Ablation studies confirm that analogical organization and contrastive structure improve performance, consistently surpassing randomly shuffled baselines across architectures. Cross-phenomenon validation using unspecified object alternations replicates these efficiency gains, confirming approach robustness. Our results show that analogical paradigm organization enables competitive linguistic rule learning with orders of magnitude less data than conventional approaches require.

Analogical Structure, Minimal Contextual Cues and Contrastive Distractors: Input Design for Sample-Efficient Linguistic Rule Induction

Large language models (LLMs) are increasingly applied to tasks involving causal reasoning. However, current benchmarks often rely on string matching or surface-level metrics that fail to assess whether a model's output is formally valid under causal semantics. We propose DoVerifier, a symbolic verification framework that checks whether LLM-generated causal expressions are derivable from a given causal graph using rules from do-calculus and probability theory. This allows us to recover correct answers that would otherwise be marked incorrect due to superficial differences. Evaluations on synthetic data and causal QA benchmarks show that DoVerifier more accurately captures semantic correctness than standard metrics, offering a more rigorous and informative way to evaluate LLMs on causal tasks.

Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification

Detecting mental health crisis situations such as suicide ideation, rape, domestic violence, child abuse, and sexual harassment is a critical yet underexplored challenge for language models. When such situations arise during user–model interactions, models must reliably flag them, as failure to do so can have serious consequences. In this work, we introduce CRADLE BENCH, a benchmark for multi-faceted crisis detection. Unlike previous efforts that focus on a limited set of crisis types, our benchmark covers seven types defined in line with clinical standards and is the first to incorporate temporal labels. Our benchmark provides 600 clinician-annotated evaluation examples and 420 development examples, together with a training corpus of around 4K examples automatically labeled using a majority-vote ensemble of multiple language models, which significantly outperforms single-model annotation. We further fine-tune six crisis detection models on subsets defined by consensus and unanimous ensemble agreement, providing complementary models trained under different agreement criteria.Content warning: This paper discusses sensitive topics such as suicide ideation, self-harm, rape, domestic violence, and child abuse.

CRADLE Bench: A Clinician-Annotated Benchmark for Multi-Faceted Mental Health Crisis and Safety Risk Detection

Chain-of-Thought (CoT) prompting has emerged as a powerful technique for enhancing language model's reasoning capabilities. However, generating long and correct CoT trajectories is challenging. Recent studies have demonstrated that Looped Transformers, a standard Transformer with cross-block parameter-sharing architecture, possess remarkable length generalization capabilities, but their limited generality and adaptability prevent them from serving as an alternative to auto-regressive solutions. To better leverage the strengths of Looped Transformers, we propose **RELAY** (**RE**asoning through **L**oop **A**lignment iterativel**Y**). Specifically, we align the steps of Chain-of-Thought (CoT) reasoning with loop iterations and apply intermediate supervision during the training of Looped Transformers. This additional iteration-wise supervision not only preserves the Looped Transformer's ability for length generalization but also enables it to predict CoT reasoning steps for unseen data. Therefore, we leverage this Looped Transformer to generate accurate reasoning chains for complex problems that exceed the training length, which will then be used to fine-tune an auto-regressive model. We conduct extensive experiments, and the results demonstrate the effectiveness of our approach, with significant improvements in the performance of the auto-regressive model.

Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning

Medical multiple-choice question answering (MCQA) benchmarks report near-human accuracy, with some approaching saturation and fueling claims of clinical readiness. Yet a single accuracy score is a poor proxy for competence: models that change answers under minor perturbations cannot be considered reliable. We argue that reliability underpins accuracyвЂ“only consistent predictions make correctness meaningful. To address this, we release ReMedQA, a benchmark suite that augments three standard medical MCQA datasets with open-answer variants and systematically perturbed items. Building on this design, we introduce ReAcc and ReCon, two reliability metrics: ReAcc measures the proportion of questions answered correctly across all variations, while ReCon measures the proportion answered consistently regardless of correctness. Our evaluation shows that high MCQA accuracy masks low reliability: models remain sensitive to format and perturbation changes, and domain specialization offers no robustness gain. MCQA further underestimates smaller models while inflating large ones that exploit structural cuesвЂ“with some producing correct answers without seeing the question. These findings show that, despite near-saturated accuracy, we are not yet done with medical multiple-choice benchmarks.

ReMedQA: Are We Done With Medical Multiple-Choice Benchmarks?

In the global drive toward carbon neutrality, deeply coordinated smart energy systems underpin industrial transformation, yet their interdisciplinary, fragmented, and fast-evolving expertise prevents general-purpose LLMs, lacking domain knowledge and physical-constraint awareness, from delivering precise engineering-aligned inference and generation. To address these challenges, we introduce Helios, the first large language model tailored to the smart energy domain, together with a comprehensive suite of resources to advance LLM research in this field. Specifically, we develop Enersys, a multi-agent collaborative framework for end-to-end dataset construction, through which we produce: (1) the first smart energy knowledge base, EnerBase, to enrich the model's foundational expertise; (2) the first instruction fine-tuning dataset, EnerInsruct, to strengthen performance on domain-specific downstream tasks; and (3) the first RLHF dataset, EnerReinforce, to align the model with human preferences and industry standards. Leveraging these resources, Helios undergoes large-scale pretraining, SFT, and RLHF. We also release EnerBench, the first benchmark for evaluating LLMs in smart energy scenarios, and demonstrate that our approach significantly enhances domain knowledge mastery, task execution accuracy, and alignment with human preferences.

Helios: A Foundational Language Model for Smart Energy Knowledge Reasoning and Application

LLMs are powerful generators of synthetic data, which are used for training smaller, specific models. This is especially valuable for low-resource languages, where human-labelled data is scarce but LLMs can still produce highquality text. However, LLMs differ in how useful their outputs are for training. Selecting the best LLM as a generator is challenging because extrinsic evaluation requires costly human annotations (which are often unavailable for lowresource languages), while intrinsic metrics correlate poorly with downstream performance. We introduce Round-robin Synthetic data Evaluation (RoSE), a proxy metric for selecting the best LLM generator without human test sets. RoSE trains a small model on the outputs of a candidate generator (LLM) and then evaluates it on generated synthetic examples from all other candidate LLMs. The final RoSE score is the mean performance of this small model. Across six LLMs, eleven languages, and three tasks (sentiment, topic, intent), RoSE identifies the optimal generator more often than any other intrinsic heuristics. RoSE outperforms intrinsic heuristics and comes within 0.76 percentage points of the optimal generator baseline. This result is measured in terms of downstream performance, obtained by training a small model on the chosen generator’s outputs (optimal vs. proxy-metric–selected) and evaluating it on human-labelled test data. Additionally, RoSE is the only metric to achieve a positive correlation with performance on human test data.

RoSE: Round-robin Synthetic Data Evaluation for Selecting LLM Generators without Human Test Sets

As large language models (LLMs) advance in linguistic competence, growing attention has turned to their reasoning abilities. Humans excel at domain-specific reasoning, particularly in normative rather than purely formal contexts. While previous studies have compared LLM and human reasoning, the domain specificity of LLMs remains underexplored. This study examines LLMs' conditional reasoning with deontic rules using the Wason Selection Task, a classic paradigm in cognitive science. Building on findings that human errors in this task reflect a matching bias, namely a tendency to select cards matching the lexical content of the rule, we test whether similar biases appear in LLMs using a dataset of conditional rules with negation. Results show that, like humans, LLMs reason more effectively with deontic rules and display matching bias in their error patterns. These findings indicate that LLMs exhibit domain-specific reasoning and matching bias with respect to deontic rules, providing insights into how their reasoning relates to human cognition.

Evaluation of Deontic Conditional Reasoning in Large Language Models: The Case of Wason's Selection Task

Attribution and fact verification are critical challenges in natural language processing for assessing information reliability. While automated systems and Large Language Models (LLMs) aim to retrieve and select concise evidence to support or refute claims, they often present users with either insufficient or overly redundant information, leading to inefficient and error-prone verification. To address this, we propose Evidence Ranking, a novel task that prioritizes presenting sufficient information as early as possible in a ranked list. This minimizes user reading effort while still making all available evidence accessible for sequential verification. We compare two approaches for the new ranking task: one-shot ranking and incremental ranking. We introduce a new evaluation framework, inspired by information retrieval metrics, and construct a unified benchmark by aggregating existing fact verification datasets. Extensive experiments with diverse models show that incremental ranking strategies better capture complementary evidence and that LLM-based methods outperform shallower baselines, while still facing challenges in balancing sufficiency and redundancy. Compared to evidence selection, we conduct a controlled user study  and demonstrate that evidence ranking both reduces reading effort and improves verification. This work provides a foundational step toward  more interpretable, efficient, and user-aligned information verification systems.

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MULSUM: A Multimodal Summarization System with Vis-Aligner and Diversity-Aware Image Selection

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